Dynamic medical ecosystems and intelligence modeling

ABSTRACT

Systems and methods of embodiments comprise receiving in real-time data of parameters representing an entity. Micro plots are generated, and each micro plot comprises a plot of the data for a corresponding time period of a multitude of time periods. Each time period is cyclical. A model plot is generated to include the micro plots plotted chronologically according to the time periods. The model plot comprises a continuous helix. A prediction of a state of the entity is generated using characteristics of the model plot.

RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application No.61/934,090, filed Jan. 31, 2014.

This application is a continuation in part of U.S. patent applicationSer. No. 14/205,844, filed Mar. 12, 2014.

This application is a continuation in part of U.S. patent applicationSer. No. 14/212,250, filed Mar. 14, 2014.

TECHNICAL FIELD

The embodiments described herein relate generally to systems and methodsfor modeling and, more particularly, to dynamic medical ecosystemsmodeling.

BACKGROUND

The basis for organized medicine was established in approximately 400B.C. Since then the art and practice has essentially been one of singlepoint probabilistic approximation and formulation. For overtwo-millennia brief encounters with the treating physician or theirstaff has represented the pillar of established medical practice andhealthcare delivery. Medicine as a discipline in the 21st centuryclearly has had the advantage of exponential growth in healthcaretechnology particularly over the past thirty years, but at its verycore, the physician's single point probabilistic approximation andformulation remain (differential diagnosis) all but unchanged in its2500-year existence. There is a need for micronization of the future ofmedicine under a new paradigm that promises to revolutionize thepractice and delivery of healthcare.

INCORPORATION BY REFERENCE

Each patent, patent application, and/or publication mentioned in thisspecification is herein incorporated by reference in its entirety to thesame extent as if each individual patent, patent application, and/orpublication was specifically and individually indicated to beincorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example of static moments in patienttreatment practice.

FIG. 2 is a plot of the Life Cycle Line, under an embodiment.

FIG. 3 is a completed graphical representation of a Life Cycle Line of afirst individual, under an embodiment.

FIG. 4 is a completed graphical representation (post-mortem) of morbidchildhood obesity and poor impulse control that has a snowballing effecton the individual's health and wellness, under an embodiment.

FIG. 5 is a completed graphical representation of a Life Cycle Linewhere the individual is in the normal zone, and was subject to randomenvironmental factors, under an embodiment.

FIG. 6 is a block diagram showing development of the dynamic MedicalEcosystems Model (dMEM), under an embodiment.

FIG. 7 shows a flow diagram of Life Cycle Line development, under anembodiment.

FIG. 8 shows slope changes in the dMEM, under an embodiment.

FIG. 9 shows the dynamic Life Cycle Line, under an embodiment.

FIG. 10 shows a plot of a Life Cycle Line depicting an individual whodid not incorporate any kind of health monitoring into their lives, anUnmonitored Lifestyle, under an embodiment.

FIG. 11 shows a plot of a Life Cycle Line depicting a MonitoredLifestyle, under an embodiment.

FIG. 12 is an example dMEM recording of the first patient, under anembodiment.

FIG. 13 is an example dMEM recording of the second patient, under anembodiment.

FIG. 14 shows the dMEM recording of the previous seven-day compresseddata compilation of this patient's pain pattern, under an embodiment.

FIG. 15 is a plot of the cure rate of the patient having only C-spinesurgery, under an embodiment.

FIG. 16 is a plot of the cure rate of the patient having C-spine andcarpel tunnel release surgeries, under an embodiment.

FIG. 17 depicts the superimposed similarity between the pain from carpaltunnel syndrome (CTS), and recent onset cervical radiculopathy, under anembodiment.

FIG. 18 illustrates multiple end users linked to the dMEM cloud, underan embodiment.

FIG. 19 is a block diagram of the dMEM integrated with a supercomputersystem, under an embodiment.

FIG. 20 depicts a helicoid example underlying the dMEM system design,under an embodiment.

FIG. 21 is a block diagram depicting a dMEMs platform hosting thecircadian model, under an embodiment.

FIG. 22 is a block diagram depicting the dMEMs platform creating areal-time sensing and collecting system running in parallel to humanphysiology, under an embodiment.

FIG. 23 shows the dMEMs from the perspective of theapplication/nano-sensor developer, under an embodiment.

FIG. 24 shows the dMEMs from the perspective of the public end-user,under an embodiment.

FIG. 25 shows the dMEMs from the perspective of the active practicephysician, under an embodiment.

FIG. 26 shows an example of the dMEMs from the perspective of the activepractice physician when treating a patient following patient discharge,under an embodiment.

FIG. 27 shows an example of the dMEMs from the perspective of the activepractice physician when treating critical care patient, under anembodiment.

FIG. 28 is a block diagram of the dMEM integrated with medical smartsystems, under an embodiment.

FIG. 29A shows entropic linear-forces** acting upon a celestialbody/mass; create fictitious pre-conceptual** randomness (primary).

FIG. 29B shows entropic angular-forces** acting upon a celestialbody/mass; create fictitious pre-conceptual** non-randomness(secondary).

FIG. 30 is a depiction of the pre-evolutional graphic of the (1) earthmoving within the forming universe's accretion disk as a chaoticcelestial** body transitioning to [(a)+(b)], where (a) represents acosmic linear entropic force and (b) represents a cosmic angularentropic force, as described herein.

FIG. 31 demonstrates the transition in the late** accretion period to(c) (tertiary).

FIG. 32 depicts the sustained “after-wake” effect of cosmic entropicforce acting upon [(a)+(b)] and producing the energy-driven transitionto (c).

FIG. 33 depicts a helicoid matrix of core memory intelligencedevelopment.

DETAILED DESCRIPTION

The conventional practice of medicine has been one of staticformulation. Static moments or points in time (much like a “snapshot” orphoto) have been the basis for practicing modern medicine. For example,a physician may check a patient's blood pressure on two differentoccasions (office visits) over a two-week period. FIG. 1 is a blockdiagram of an example of static moments in patient treatment practice.The snapshot of information obtained during the two visits is then usedto determine and initiate a medication regime with an antihypertensiveagent. For example, a symptomatic 44-year-old overweight male patientcomplaining of a two-week history of fatigue and dizziness newlypresents to a family physician's office at 4 pm on a Friday afternoon.His blood pressure as checked by the office nurse is noted to bemarginally elevated and his screening blood profile normal. He is toldto follow up in the family physician's office in two weeks. On thesecond visit the patient's blood pressure remains elevated.

This brief momentary dual snapshot of information obtained during twoseparate visits at different times, and under different conditions, isthen used by the physician to access and briefly determine adifferential diagnosis as to the probable etiology of the patient's HighBlood Pressure. Then the physician will initiate what he or she may deemto be an appropriate medication regime, i.e. with an antihypertensiveagent three times per day.

Conventional medical treatment and diagnosis therefore leaves much to bedesired in terms of efficacy when one is establishing a diagnosis basedupon two brief office visits separated by a 14-day interval. Furthermoreinefficiency becomes evident, if not obvious, in an antiquatedmedication delivery system based on a one-size fits all approach (i.e.TID schedule). When in reality, diurnal morning and evening bloodpressure spikes may well have caused an inaccurate diagnosis. Thetroubling consequences of this, include this one-time walk-in patientnow on the street, suffering from potentially life-threatening reboundiatrogenic medication induced hypotension.

Embodiments described herein include a medical modeling system orplatform, also referred to as the “dynamic Medical Ecosystems Model”(dMEM), that redefines the practice of medicine through proprietaryprocesses of real-time dynamic medicine incorporating nano-sensors. ThedMEM creates and applies to individuals a novel real-time healthcontinuum, where generation and application can begin at the moment ofbirth. The dMEM monitors an individual's health throughout their life.As such, the dMEM provides a virtual platform, creating and enablingpreemptive-preventive self-care delivery in real-time. The dMEM alongwith technological advances in medical nano-sensors will drive the novelmedical paradigm, forever changing the scope and practice of humanhealthcare.

Although the detailed description herein contains many specifics for thepurposes of illustration, anyone of ordinary skill in the art willappreciate that many variations and alterations to the following detailsare within the scope of the embodiments described herein. Thus, thefollowing illustrative embodiments are set forth without any loss ofgenerality to, and without imposing limitations upon, the claimedinvention.

The dMEM of an embodiment develops a Life Cycle Line model, or staticlife cycle, into a dynamic living model as described in detail herein.FIG. 2 is a plot of the Life Cycle Line, under an embodiment. Inessence, a completed (static) Life cycle Line represents the lineargraph of an individual's cumulative life, beginning at birth,progressing through normal, subclinical, and clinical zones culminatingin death. In the completed cycle, the slope of an individual's LifeCycle Line may have increased or decreased as a result of life choices.The graphic indicates the male in this case, had a relatively diseasefree life. At the age of 25 he crossed from the normal zone N (nounderlying disease process) into the subclinical zone SC (no symptoms toindicate disease, but detectable disease is apparent on labs, imagingstudies, etc.; statistically, the majority of the US population enters asubclinical disease zone as early as the third decade with the onset ofclinical symptoms by the fifth decade), and by age 53 years a low-gradeclinical zone C (disease state arriving to the point of symptompresentation causing the patient to seek medical attention; this iscurrently the main point of entry into US healthcare by most first-timepatients). From age 53 years to 78 years his symptoms, in the clinicalzone gradually progressed upwards with death D occurring at 78. The LifeCycle Line ends upon the death of the corresponding individual.

The upward or downward slope of an ongoing dynamic Life Cycle Linedetermines not just the length of one's life but the quality as well. Itis anticipated that much of twenty-first century healthcare will bedirected towards assisting individuals in the proper day-to-day datamanagement of his or her dMEM on the dynamic Life Cycle Line model. Indoing so, metabolic disease states, both chronic and acute, will beeradicated with marked overall reductions in early life morbidity,disability, and death. It is presently known that well over 45% ofprogressive acute and chronic metabolic disease states in the U.S. aredue to early-learned aberrant behavioral patterns. Learned behavioralpatterns are subject to modification and can be positively modified bythe dMEM, resulting in dramatic improvements in overall lifelong healthand wellbeing.

Detailed examples are presented below to illustrate how the Life CycleLine may appear in three different people, with three distinct causes ofdeath. FIG. 3 is a completed graphical representation of a Life CycleLine of a first individual, under an embodiment. The individual of thisexample has properly cared for their health and wellness, living to anage of 90 years with great quality of life, under an embodiment. Even inan individual who is healthy, the natural course of events range fromnormal at birth, with varying transitions into sub-clinical and clinicaldisease prior to death. The 90-year-old individual maintained a greatquality of life, without significant lifetime clinical (symptomatic)disease, as per her Life Cycle Line. Although subclinical (asymptomatic)disease was evident from the age of 45 years until the time of herdemise, death in this case occurred due to an unexpected head-traumafrom a fall resulting in a massive hemorrhagic stoke.

FIG. 4 is a completed graphical representation (post-mortem) of morbidchildhood obesity and poor impulse control that has a snowballing effecton the individual's health and wellness, under an embodiment. At age tenyears, the individual crosses into the subclinical zone (elevated bloodsugars), eventually crossing into the clinical zone by age 20 with adultonset insulin dependent diabetes. The ability to directly quantify aperson's everyday actions into one output is a key concept in the LifeCycle Line.

FIG. 5 is a completed graphical representation of a Life Cycle Linewhere the individual is in the normal zone, and was subject to randomenvironmental factors, under an embodiment. His Life Cycle Line wentfrom the normal zone vertically through subclinical and clinical zonesinto death. He was a healthy twenty-year old who was originallyprojected to live for at least 83 years. This individual's life was cutdrastically short, killed in action in Afghanistan at age 20.

Consideration of the linear graphical components described hereinprovides context for the more complex underlying processes of the dMEMof an embodiment. For the sake of the description herein, the followingexample will employ a hundred year life. FIG. 6 is a block diagramshowing development of the dMEM, under an embodiment. A hundred years onthe sloping lifeline in this model will equal 36,500 days of life,representing a single Life Cycle Line A (continuous uninterrupted lifecycle line) of 36,500 connecting points B (composite points thatcomprise continuous line A). Hence a single point on the Life Cycle Linerepresents one day or a completed 24-hour cycle, which equates to a360-degree circle. It is upon this circle the nano-monitored events orphysiological data of the previous 24-hour period is recorded andanalyzed. The 24-hour event cycle is in reality a continuous helix inthe dMEM process as described in detail herein. It returns to the samebeginning point every 24 hours, but due to the passing of time it islocated at a slightly different point in space.

With reference to FIG. 6, the basis for the dMEM process model orcomputer model arises from C (each completed cycle on the helixrepresents a composite point on B) and D (each mapped 24-hour cycle(e.g., 8 am to 8 am) represents a composite revolution on C) in thediagram showing dMEM development. The dMEM of an embodiment includes a24-hour cycling processor-based (e.g., server, cloud, personal computer,etc.) platform that collects real-time multiples of physiologic datafrom medical micro sensors (external or internal), while using thecyclic models of C and D, to essentially change the Life Cycle Line froma static to a dynamic entity. The helical recordings of each 24-hourcycle maintain all medical data corresponding to an individual. Daily24-hour cyclic recordings can then be plotted to a patient's Life CycleLine. FIG. 7 shows a flow diagram of Life Cycle Line development, underan embodiment.

More particularly, the dMEM receives physiological data that includesdata of multiple physiological parameters collected in real-time fromsensors coupled to an individual subject. The sensors are coupled to orimplanted in the subject, and are configured to telemeter thephysiological data to the dMEM platform or otherwise offload or downloadthe physiological data to the dMEM platform. The sensors of anembodiment include sensors of any type and/or configuration asappropriate to collection of physiological data from a living entity.Furthermore, the physiological data includes any data or parameterscapable of being collected from a human subject.

Upon receiving the physiological data, the dMEM generates a number ofmicro plots, where each micro plot represents or corresponds to aparticular time period. Each micro plot includes a cyclical plot of thephysiological data for a corresponding time period (e.g., 24-hourperiod, etc.). Thus, each micro plot comprises an integrated plot of allphysiological data collected from a subject during the correspondingtime period. Using the micro plots, the dMEM generates a medical modelplot, or Life Cycle Line. In generating the medical model plot, the dMEMplots the micro plots chronologically according to the correspondingtime periods, such that a location of an endpoint of each micro plotdetermines a change in slope of the medical model plot. As described indetail herein, the slope of the medical model plot represents a state ofhealth of the human subject.

The successful dMEM from 0 to 90 years equals a connected series of32,850 points representative of a continuum of days. Furthermore, eachpoint may then be reduced to a non-random reoccurring 24-hour cycle thatessentially returns to the same position after completion of a 360°rotation over 24 hours. Each hour represents 1/788,400^(th) of 90 yearsand equates to 15° rotation on each 360° cycle. Each rotation returns tothe same point in the 360° rotation but has moved in space to a newposition representing the end of one 24-hour cycle and the beginning ofa new 24-hour cycle. Hence the dMEM exists as a helical entity in space.

The point in space that ends each monitored cycle, determines the changeof slope in the dMEM. This slope change correlates to the actions andbehaviors of the preceding 24-hour cycle. The dMEM precisely displayschanges in an individual's monitored actions and choices for theupcoming day, based upon the choices made the day before. FIG. 8 showsslope changes in the dMEM, under an embodiment. Representative driversfor an increased slope (less time in the normal zone, shorter life,worse quality of life) include, but are not limited to, the following:aberrant behavioral patterns; excessive alcohol consumption; unhealthy,unbalanced diet; tobacco use; sedentary lifestyle; depression; loweducation levels; living at or below the poverty level. Representativedrivers for a decreased slope (more time in normal range, longer life,better quality of life) include, but are not limited to, the following:no alcohol consumption; healthy balanced caloric diet; no tobacco use;active lifestyle; good mental hygiene; higher education levels; livingabove poverty level.

Therefore, the plotting of each 24-hour cycle of monitored physical andmetabolic parameters and changes produces a continuous helixrepresenting the true dynamic nature of the dMEM. By dissecting thehelix, each previous and succeeding monitored cycle can be directlycompared with others. For example, once a baseline of seven consecutivecycles is obtained, these may be sequentially compressed to a singlecycle, yielding a weekly compilation. Uniform compression of a month,year, or decade becomes possible as aging data becomes available forcompression. The end-user will have multi-sourced feedback availablecontinuously.

New medical paradigms are emerging of which the dMEM will be a majorcomponent. For example, a first medical paradigm is one in whichpreemptive “self-healthcare” will virtually eradicate acute and chronicdisease states. A second medical paradigm is one that reveals previouslyundiagnosed disease, significantly augmenting future medical andsurgical outcomes. A third medical paradigm is one in which futuredynamic medical, biomedical, pharmacological, academic, andepidemiologic research and stratification, changes the face of globalhealth. A fourth medical paradigm is one in which supercomputers,physiology, and medicine become a singular dynamic real-time continuum.FIG. 9 shows the dynamic Life Cycle Line, under an embodiment. The LifeCycle Line demonstrates the future point of entry, integration, and flowof the emerging paradigms (I, II, III, IV) in the healthcare continuum.A detailed description of the new medical paradigms follows.

With reference to the first new medical paradigm, an example of the dMEMas a preemptive “self-healthcare” model can be demonstrated bycomparison of the following completed Life Cycle Lines. The dMEM of anembodiment virtually eradicates acute and chronic disease states. FIG.10 shows a plot of a Life Cycle Line depicting an individual who did notincorporate any kind of health monitoring into their lives, anUnmonitored Lifestyle, under an embodiment. Thus, they did not havefeedback regarding how their day-to-day choices truly impacted theirfuture health and longevity. This individual suffered from a massiveheart attack at age 40 years due to his aberrant behavioral patterns. Hesurvived the event, but as noted in the Life Cycle Line, from age 40years until death at age 60 years, the patient remained permanently andtotally disabled, dependent upon government resources.

FIG. 11 shows a plot of a Life Cycle Line depicting a MonitoredLifestyle, under an embodiment. This individual's health was monitoredfrom age 10 years. The data collected continuously from the individualwas processed via the dMEM to yield his Life Cycle Line. From the timethis individual was a child, he and his parents had the benefit ofknowing how his (and his parents') choices were impacting him. The childwould learn from a much earlier age which choices in his life are trulyhealthy. The visual feedback from the Life Cycle Line would providepositive reinforcement for healthful living from a very young age. Thispreemptive “self-healthcare” would necessarily eliminate 45% ofdebilitating acute and chronic metabolic disease states in the U.S.Healthcare dollar savings would be tremendous. With the MonitoredLifestyle, the patient was able to see an improved quality of life,improved longevity, and productive lifestyle with absence of disability,until death at 74.

Under the second new medical paradigm, in the diagnosis and treatment ofexisting acute and chronic disease states, a shift occurs frompreemptive preventive medicine, to one of treatment of establisheddisease, as osteoarthritis, rheumatoid arthritis, carpal tunnelsyndrome, and cervical radiculopathy are explored.

In a first example under the second new medical paradigm, two60-year-old male patients, new to the doctor's office on the same day,complain of generalized aches and pains consistent with arthritis. Thetendency is to treat them medically based on a “snapshot moment in time”office visit. In this case they both would likely be treatedsymptomatically with anti-inflammatory medication and sent home. Whenthe monitored cyclic dMEM is applied on a real-time 24-hour cycle forseven days and then compressed, two distinctly different patterns ofpain begin to emerge. The first patient would elicit a pattern analogousto rheumatoid arthritis with progressive pain at its zenith in the earlymorning hours, as seen in the patient's dMEM recording. FIG. 12 is anexample dMEM recording of the first patient, under an embodiment.

The second patient's pain pattern will demonstrate its true zenith inthe mid afternoon, which would indicate degenerative arthritis, as seenin the patient's dMEM recording. FIG. 13 is an example dMEM recording ofthe second patient, under an embodiment. This becomes apparent in thereal-time recurrent cycling “movie” while not recognized nor likelyconsidered by modern day “snapshot” medicine. Both patients aremisdiagnosed as a result, and neither receives accurate or appropriatecare.

The relative differences seen between the dMEM recording of theseven-day compressed data compilation (FIG. 12) of a pain pattern in thefirst patient with rheumatoid arthritis, and the dMEM recording of theseven-day compressed data compilation (FIG. 13) of a pain pattern of thesecond patient with degenerative arthritis shows that the treatments foractive rheumatoid arthritis are vastly different than the prescriptionfor anti-inflammatories the patient was given. With the use of dMEMmonitoring, the previously unseen physician errors, misdiagnoses, andinappropriate treatments are quickly revealed.

A 45year-old patient presenting with radiating neck, arm, and hand painrepresents a second example under the second new medical paradigm. Hewas recently involved in a motor vehicle accident (MVA) and has cervicalradiculopathy, and is currently awaiting C-spine surgery in five days,as recommended by his neurosurgeon. The patient worked as a dieselmechanic, and had well documented pre-existing occasional hand painradiating into digits 1, 2, 3, and forearm prior to the MVA. He now hassevere hand pain running into digits 1, 2, 3, with forearm, arm, andneck pain. FIG. 14 shows the dMEM recording of the previous seven-daycompressed data compilation of this patient's pain pattern, under anembodiment. The pain clearly varies during course of the day. The dottedelevation in pain is denoted as primarily sharp neck, shoulder, andforearm pain radiating into the hand. The solid red markers indicatedull pain occurring primarily in the hand and forearm.

The pain diagram, to an astute clinician using the dMEM, will be obviousand can be easily compared to stored database renderings to confirm thediagnosis, with a probability nearing one. The patient, in reality, haspre-existing low-grade carpel tunnel syndrome that is now acutesecondary to the double crush from MVA induced acute cervicalradiculopathy. The patient has two diagnoses and will need twosurgeries: a C-spine surgery and carpel tunnel release before he willget total relief

Without the dynamic dMEM compressions he would have likely beendiagnosed and treated with C-spine surgery only. His cure rate wouldhave been reduced to 33% with chronic pain and ongoing disability untildeath. FIG. 15 is a plot of the cure rate of the patient having onlyC-spine surgery, under an embodiment.

If both diagnoses had been made and both surgeries performed, his curerate would have been 85%, with minimal short-term disability. FIG. 16 isa plot of the cure rate of the patient having C-spine and carpel tunnelrelease surgeries, under an embodiment.

FIG. 17 depicts the superimposed similarity between the pain from carpaltunnel syndrome (CTS) (right-tilted oval), and recent onset cervicalradiculopathy (C6) (left-tilted oval), under an embodiment. The C6 ovalillustrates the treating physician's assumed single diagnosis based onMRI changes consistent with cervical radiculopathy, but furtherassessment via dMEM compressions would have clearly revealed a dualoverlapping diagnosis (crossed ovals) of cervical radiculopathy C6 andsecondary carpel tunnel syndrome (CTS), creating a double crushphenomenon.

Referring to the third new medical paradigm, FIG. 18 illustratesmultiple end users linked to the dMEM cloud, under an embodiment. Thisallows for scalable real-time data acquisition. Multiple end userinstitutions (medical, academic, among others) under this embodimentselect from a number of parameters they wish to monitor or study inreal-time. For example, in the diagram below, the hypothetical plane mayrepresent an institution's selected area of current study. This couldinclude geographical distribution, age distribution, race distribution,disease prevalence, etc. All of these parameters and more are monitoredin real time using the dMEM.

Drug companies, for example, will use the dMEM to monitor multiplecohorts of study participants in ongoing real-time clinical trials. Thiswill undoubtedly change the dynamic of clinical drug trials with theearliest yet recognition of a drug's efficacy, safety, as well asunanticipated positive or negative collateral side effects.

Referring to the fourth new medical paradigm, the integration of asupercomputer system into the dMEM ensures that every individual,patient, hospital, and medical institution in the world will have acontinuous open-ended flow of real-time input and data collection fromglobal supercomputer guided diagnostics and treatment. FIG. 19 is ablock diagram of the dMEM integrated with a supercomputer system, underan embodiment.

In the past three decades, U.S. Healthcare has experienced exponentialtechnological growth in “linear” diagnostic imaging and treatmentsystems. These advances have consistently been directed and ultimatelydesigned to assess and or treat established pre-existing acute andchronic disease states, often as design-specific “post-event” diagnosticand treatment modalities i.e. heart, stroke, cancer care, etc.

For this reason, all diagnostics developed and introduced in the pastthirty years tend to cluster around the after-the-fact points ofclinical presentation due to chronic dysfunctional and acute eventoccurrence (heart attack). No significant preventive measures of anykind have been able to change the paradigm to this point in time. Duringthis same thirty-year period, the medical dollar spent on design anddevelopment of pre-emptive preventable disease management lagged farbehind. This was particularly true for the concept of medical ecosystemsdevelopment until recent advancements in medical nano-sensorsestablished a real and present niche-need. Nevertheless, it iscalculated that a rapid paradigm shift to multi-dimensional medicalecosystem will significantly impact the anticipated growth curve in“linear” healthcare research and development over the next two decades.This will be particularly evident as the scalable medical ecosystems ofthe dMEM provide and guide individual health and healthcare delivery ona real-time ‘day to day’ basis by the application of its preemptivemedical capabilities.

The state of medical nanotechnology is evolving rapidly, and themultiplicity of nano-sensor and sensor derivatives expected to enter themarket over the course of the next five years will see exponentialgrowth in numbers. Diversity of development, sensitivity and continueddiminution in size will ensure that an expanding array of disparatetechnical and medical applications will continually be available to themobile general public. However, in the coming years as industry maturityoccurs, the surviving spectrum of segmented medical apps will be forcedto unify and standardize across the board before nanotechnology as aemerging field in medicine may flourish.

The conventional novelty applications running at any one time measuringan individual's vital signs may suffice for the younger health-orientedsegment of society. These applications, or apps, can be configured tocontinuously monitor for a “triggering event” such as an irregularheartbeat while an end-user is exercising. At that point where theanomaly is sensed, the data capture on the end user can increase, byinitiating other apps (e.g., an app for cardiac enzymes) to monitorassociated parameters.

That said, to become a fully integrated adjunct in the future of bothpreemptive-preventive and acute critical care medicine, the systems ofthe dMEM of an embodiment are configured to run and monitor 60 to 200and more integrated real-time apps on an ongoing 24/7 basis. As aresult, the dMEM makes use of a mammoth data collection-compressionarchitecture with sensitivity extending well beyond linear and planarmappings of 24 hours. The computing hardware, storage and bandwidth forsuch an endeavor is readily available with cloud-based web-services anddata-centers offered by third party providers. The limiting factors willnot be computer or hardware capacities, but rather innovativeconfiguration and integration. Medical nano-sensors combined with thedimensionality of real-time human physiology will push present computingarchitectures into a multi-dimensional framework.

Since the beginning of time, intelligent life on earth has beendependent and unknowingly subservient to cyclic patterns (daily,monthly, yearly). The most obvious of these patterns is the 24-hourcircadian cycle, established by earth's rotation. This perpetuallyreoccurring 24-hour cycle has had countless millions of yearsprogramming human life to respond and thrive upon a cycled existence.FIG. 20 depicts a helicoid example underlying the dMEM system design,under an embodiment.

The architecture and running system of embodiments described herein givemuch attention and consideration to a three-dimensional (3D) compositeworld, to run parallel with real-time physiologic data capture inconjunction with person place time and event. FIG. 21 is a block diagramdepicting a dMEMs platform hosting the circadian model, under anembodiment.

A cloud driven helical architecture is a paradigm changer for the futureof medicine. FIG. 22 is a block diagram depicting the dMEMs platformcreating a real-time sensing and collecting system running in parallelto human physiology, under an embodiment. Standardization ofpre-configured plug and play ports to the cloud platform, thenano-sensor hardware developer need only configure sensor software tointerface with the cloud's ports. Each medical nano-sensor developer andtheir respective software engineers will be provided hands-on tutorialsand technical assistance to grasp a thorough understanding of the 3Dreal-time architecture and the 24/7 operating systems requirements.

FIG. 23 shows the dMEMs from the perspective of theapplication/nano-sensor developer, under an embodiment. Approaching theembodiments described herein from the perspective of theapplication/nano-sensor developer, when the app/nano-sensor is approvedand selected for port to real-time system migration, the accompanyingnano-sensor/app becomes available on the cloud platform to be downloadedand applied to the end users handheld or tablet device. Eachapp/nano-sensor will be dormant on the cloud until an end-usersinterface is activated and usage begins.

FIG. 24 shows the dMEMs from the perspective of the public end-user,under an embodiment. Approaching the embodiments described herein fromthe perspective of the public end-user, when a new cloud account inopened by an individual, he or she may then, depending uponcredentialing be given access to select from approved app/nano-sensorthat may be appropriate for public usage. These will be listed on thecloud-based open public interface, much like an app store. Each app willprovide a detailed medically oriented description of available usage forthe potential end-user, as well as bundling capabilities, bandwidthneeds, ordering instructions for hardware, cloud fees, etc. The siteowner may also select to provide viewing rights to other family members,various care providers such as physicians, nurses, home healthproviders, emergency services providers, hospitals and researchinstitutions, etc. Categories of public self-tracking users include theyoung health conscious adult who wants daily tracking of basic vitalhealth systems linked and plotted to the Life Cycle Line. Monitoredusers as a category, may be nursing home patients tethered to familyphysician, hospital, home health, family members, as well as yet to becreated general and specialty monitoring systems.

FIG. 25 shows the dMEMs from the perspective of the active practicephysician, under an embodiment. Approaching the embodiments describedherein from the perspective of the active practice physician, when a newaccount is opened in his or her name and credential verification hasoccurred the physician is given direct access to appropriate(non-public) medical apps commensurate to his/her specialty andtraining. He or she will be able to potentially link-in to his patient'sexisting user site and add medically monitored sensors that extendbeyond normal public access.

This, for example, may occur upon hospital discharge of a known patientwho had been hospitalized for two weeks in acute congestive heartfailure. FIG. 26 shows an example of the dMEMs from the perspective ofthe active practice physician when treating a patient following patientdischarge, under an embodiment. The treating cardiologist, in this case,upon patient discharge may wish to continue to follow real-time heartindices post-discharge for two to three weeks. By extending real-timemonitoring beyond the hospital stay to the treating physicians handheldor tablet device (perhaps even professional monitoring services), dailymedication changes as may become needed would negate what would surelybecome a hospital re-admission for a similar non-monitored patient.

In another example, a 33-year old female patient is transferred from theEmergency Department to the Critical Care Unit after initial assessmentindicates the patient has sustained multi-trauma from a motor vehicleaccident one hour earlier. CT Scans on admit to the ER reveal nointra-abdominal or intra-cerebral bleeds, but renal, splenic and hepaticcontusions are suspected. The patient has multiple rib fractures, and isbreathing on her own and semi-comatose. The admitting Critical Carephysician has been apprised of the patient's condition after reading thepatient's electronic chart from the emergency department while thepatient is in transit to the Critical Care Unit. Upon arrival heperforms a complete physical exam. At that juncture, the physician notesthe patient has already been identified and has been logged intoHospital's Cloud Port on the 3D Cloud platform.

From that point the physician determine what body systems are of mostimmediate importance to monitor. He will have a handheld tablet with aselection list of medical systems categorized app icons to choose from.The list will have hundreds of individual monitors to choose from aswell as lists of single app consolidated nano-sensors. He will make hisdecision promptly and upon touchpad app selection he will be activatingthe helical cloud system for immediate recording and feedback. As eachnano-sensor or consolidated group of nano-sensors is applied, immediatereal-time bedside feedback monitoring is initiated from the Cloud. Thephysician may select upwards of 50 or more nano-sensors to monitormulti-body systems (e.g., real-time hepatic enzyme flows, cardiacenzymes, renal functions, etc.) all in an effort to preemptively monitorfor latent contusional blood loss that could preemptively indicatepending catastrophic organ failure. As the patient's medical conditionstabilizes and improves over the next 48 hours the numbers of acuteadmission (50) nano-sensor functions being monitored may be graduallypruned as condition allows. FIG. 27 shows an example of the dMEMs fromthe perspective of the active practice physician when treating criticalcare patient, under an embodiment. The above are just a few citedexamples, and in no way are an indication of all potential systemsusers.

In the above case presentation the medically-necessary selections ofnano-sensors were made by the attending acute care physician butembodiments are not so limited. It is promptly anticipated that nearfuture joint venture projects with potential medical smart systems suchas IBM's Watson, or comparable system, may allow for the integration ofcomputer assisted diagnosing, as well as computer monitored patient carewith eventual real-time computer to patient monitored management formultiple systems life support including medication delivery. FIG. 28 isa block diagram of the dMEM integrated with medical smart systems, underan embodiment.

Genesis of Early and Latent Intelligence

Probability modeling is considered key to the anticipated future arrivalof Artificial General Intelligence. Previously, efforts to study causalgeneration of probability as it may apply to milestones in humanevolutional intelligence have not occurred. In the description thatfollows, probability models are used to constructively examine thedistant-past pre-organic, pre-biologic, pre-conceptual, pre-consciousand pre-probabilistic beginning of human intelligence. In so doing, thecosmos' causal origins for the earth-bound effect of evolution may befound.

The modeling described herein comprises or at least relates toprobability modeling that is integral to the arrival of ArtificialGeneral Intelligence. The following presents a description of artificialintelligence (AI) probability models to examine the roots of earlyintelligence as well as the connections and pathways to future AI.

Understanding the observer frame of reference, as it applies to inertiaframes and non-inertia frames is paramount to a final understanding ofcontext and content of the following description. The definitions thatimmediately follow correspond to a non-inertia frame of reference(non-inertial frames of reference when deemed necessary are denotedherein by “*”) and are explicitly related to the perspective of anobserver on the surface of earth i.e. celestial body:

The term “concept” as used herein includes but is not limited tosomething formed in the conscious mind, for example a thought or notion;

The term “conceptual” as used herein includes but is not limited to of,relating to, or based on conscious mental concepts;

The term “pre-conceptual” as used herein includes but is not limited toof, relating to, a period or time prior to the existence ofconsciousness, conscious thought, conscious mental concepts orconceptualization;

The term “random probability” as used herein includes but is not limitedto of, relating to, or based on a conscious concept derived from asingle present or prior mental or physical event;

The term “non-random recurring probability” as used herein includes butis not limited to of, relating to, or based on a conscious conceptderived from relational prior mental or physical events; and

The term “effectual probability” as used herein includes but is notlimited to of, relating to, or based on conscious mental concept derivedfrom a prior mental or physical event that can physically direct and/oract upon an immediate present or future physical event.

The definitions that immediately follow correspond to an inertial frameof reference (inertial frames of reference when deemed necessary aredenoted herein by “*”) and are explicitly related to an observer locatedin the open space of the cosmos:

The term “pre-conceptual” as used herein includes but is not limited toof, relating to, the sole interactions of physical components generatinga conceptual figment that necessarily precedes any prior existence ofconsciousness, conscious thought, conscious mental concepts orconceptualization. It is the pre-evolutional cosmos' physical creationof a precursor to intelligence; and

The term(s) “random and non-random recurring probability” as used hereinmay pre-conceptually appear as physical entities only (entropic forcesupon mass).

The modeling described herein is based on a hypothesis that evolutionemerged on earth as an effect, and its causal origins are to be found ina pre-evolutional cosmos devoid of the presence or benefit ofprobability, necessity, purpose, organization, memory or consciousintelligence. The description herein examines evolution as the beginningpoint of an effect (non-inertial frame of reference) rather than thepoint of origin of a cause (inertial frame of reference). To fullyappreciate causation and effect under this conjecture, the onset ofbiologic evolution must necessarily be assessed one-step further removedfrom its own effectual origin to encounter a two-pronged causalbeginning. This is accomplished by generating a thought experimentlooking back to the distant-past spanning large expanses in an entropicdriven cosmos from the perspective of earth-bound observers.

The top-down search for causation, is as follows:

A. De-construct the process of biologic evolution for an earth-boundobserver by going back billions of years to the onset of earth'srotational cycles in an early post-accretion solar system;

B. From this effectual* point on earth in a post-accretion solar system,probability modeling as a tool is then used once again, looking furtherback to an entropic pre-biologic, pre-conceptual and pre-probabilisticenvironment in the late recombinant period**. At this point, particulatemass coalescing by action of linear and angular forces gives rise tolarge-scale galaxy and star formations in the cosmos. It is here thatlinear and angular entropic force acting upon a celestial body givesconceptual rise to random and non-random probability;

C. These individually may be looked upon as a fictitious pre-conceptualeffect that encompasses all celestial* bodies that are transitioning toa stabilized rotation;

D. These when acting in concert upon a celestial body in stable rotationbecome causal** leading to the onset of a celestial* effect with theeventual rise and propagation of evolutionally induced intelligence.(i.e., as has occurred on planetary earth).

This top-down dissection into an early entropic driven universe**reveals a pre-evolutional, pre-biologic environment starkly devoid ofthe presence or benefits of necessity, purpose, organization, memory orintelligence. When mindfully re-constructing from a bottom-upperspective, energy and mass in motion are the only substrates**available to build toward an effectual* evolutional earth model.Entropic forces acting upon celestial bodies give rise to minimalafter-traces of occult probability as the rise of early randomness andlater non-randomness generated in the after-wake of energy acting uponmass appear to be new found examples of conceptual fictitiousforces/effects*. These subtle fictitious after-effects created by forceacting upon mass are the only available bridging tools to advance apre-evolutional physical environment to an evolutionallyconceptual/intellectual one, and as such, these pre-conceptual-effectsrepresent the earliest rudimentary principles for what later developsinto intelligence**.

The fictitious generation of random and nonrandom recurring probabilitycreates the link between the early physical and later conceptual,effectual and eventually intellectual. In bridging a bottom-uppre-evolutional** period to a post-evolutional* period, the two-prongedcosmic causals** of entropic driven primary randomness, and secondarynon-randomness, when combined, become the drivers for the fictitiousrise of a single, new-order tertiary* probability model that iseffectual* as it allows and promotes conscious intelligence overaccountable time. It is the beginning point of the process of evolution,as it is known.

The objectives include: identifying the pre-evolutional, inorganic,non-biologic physical antecedents that caused** the effectual* emergenceof human intelligence on earth; and identifying and defining thepre-evolutional inorganic causes** and the post-evolutionalinorganic/organic causes* that will lead to an effectual* emergence ofartificial general intelligence on earth.

Considering the objectives, general probability modeling is mindfullyapplied and is used exclusively to compare, examine and categorize thecomponents of our distant-past earth in a top-down non-inertial frameand then in a bottom-up inertial frame approach.

Regarding the top-down non-inertial frame in pursuit of an earlypost-evolutional environment, two distinct interacting models areencountered and referred to herein as the “Earth proper” (as a celestialbody), and the “Distant Cosmos”. The Earth proper (as a celestial body)is a stable surface environment serving as a potential self-containedcauldron capable of hosting an ongoing and infinite array of perpetualrandom occurrences (energy consuming/yielding of chemical, mechanical,electrical, and magnetic inter-reactions in varying states andtransitions i.e. gaseous, liquid and solid). Note that randomprobability emerged on earth in an after-wake of entropic linear forcesin a pre-evolutional cosmos, and is perpetual, but ineffectual as tofuture change.

The Distant Cosmos is a space continuum and as an entropic energy/powersource influences earth's non-random recurring gravitational rotation inspace. Note that non-random recurring probability emerged on earth inthe sustained after-wake of entropic angular rotation in apre-evolutional cosmos, and is perpetual, but is ineffectual as tofuture change.

Regarding the bottom-up inertial frame, from a pre-evolutional,pre-biologic, pre-conceptual perspective, two distinct models may beencountered. When using probability modeling to examine the earth, as acelestial body in a now pre-evolutional cosmos, two distinctpre-conceptual** occurrences are again immediately recognized as randomprobability and non-random recurring probability. These are the resultof causal entropic forces** acting upon a rotating (1) earth in aphysical (2) cosmos. Remarkably, these two fictitious “pre-concepts” aresolely created by the after-wake effect of mass and energy in motion.They are denoted as,

(a)+(b),

where (a) represents entropic linear-forces** acting upon a celestialbody/mass; create fictitious pre-conceptual** randomness (primary), and(b) represents entropic angular-forces** acting upon a celestialbody/mass; create fictitious pre-conceptual** non-randomness(secondary).

Some 4.6 billion years ago the solar system was in the process offorming by accretion. It is likely that during this period thepre-evolutional interaction of celestial bodies i.e. the (1) earth andforces of the (2) cosmos, gave rise in an “after-wake” effect of linearand angular entropic forces to the individual generation of causal** (a)randomness, and (b) non-randomness on (1) earth. FIG. 29A shows entropiclinear-forces** acting upon a celestial body/mass; create fictitiouspre-conceptual** randomness (primary). FIG. 29B shows entropicangular-forces** acting upon a celestial body/mass; create fictitiouspre-conceptual** non-randomness (secondary).

FIG. 30 is a depiction of the pre-evolutional graphic of the (1) earthmoving within the forming universe's accretion disk as a chaoticcelestial** body transitioning to [(a)+(b)], where (a) represents acosmic linear entropic force and (b) represents a cosmic angularentropic force, as described herein. FIG. 31 demonstrates the transitionin the late** accretion period to (c) (tertiary). The sustained“after-wake” effect of cosmic entropic force acting upon [(a)+(b)]results in the energy-driven transition to (c); only when thecelestial** body i.e. earth in this case, experiences aligned linear andangular forces that are perpendicular. FIG. 32 depicts the sustained“after-wake” effect of cosmic entropic force acting upon [(a)+(b)] andproducing the energy-driven transition to (c). The tertiary state isultimately the end-result and represents the earth's* point of entryinto a stable energy driven post-evolutional era giving rise to thecreation of a higher-order tertiary* probability with a fictitiousforce/effect that is now consistent with and capable of generatingeffectual* probability.

With the perpetual constancy of causal entropic forces acting uponearth* (i.e. linear and angular forces) the resulting after-wake ofperpendicularly aligned [(a)+(b)] necessarily transition to a standingenergy driven vortical helix denoted as (c). The use of a helicalanalogy allows and defines the pre-evolutional/post-evolutional pointwhere the fictitious effects of entropy driven pre-conceptual random andpre-conceptual non-random merge as they move to a single tertiary-order*probability model (R-N-R and N-R-N). The tertiary probability* modelthat is created conceptually, perceptually and effectually establishesaccountable time (t) on earth. The then recurring 24-hour circadiansequencing of 1(c) immediately transitions to 1(c)/t as it becomes adirectional thermodynamic time arrow. It is the unification ofpre-conceptual random and nonrandom probability in the after-wake ofcausal entropic forces acting upon celestial mass that creates thisperpetual helicoidal matrix that enables development over billions ofyears of organic intelligence*, as up/down memory and purposefulpredictive intelligence now become conceptually, perceptually andeffectually possible across time. FIG. 33 depicts a helicoid matrix ofcore memory intelligence development.

The slow but progressive multi-billion year rise of conscious* biologicintelligence in response to the (c)t perpetual chaos and uncertaintynecessarily occurs due to the nature of incremental genetically accruedintelligence. Having now arrived to this point, the human creation andpropagation of AGI will not be subject to such.

It is the causal interaction of the pre-evolutional tangibles** ofcosmic entropic linear, angular and rotational forces acting in concertwith cosmic mass in motion, that create an accountable real-timetertiary-probability on earth. Tertiary-Probability* as apre-evolutional/post-evolutional “effect” establishes the earth'sbiologic beginning point for the conceptual and perceptual rise of thecognitively effectual intangibles of accountable time, probability,organization, memory, purpose and progressive intelligence. Any majorchange or disruption relative to the tangibles** of the (1) earth or the(2) cosmic entropic forces acting upon it; and theafter-wake-energy-driven-effects of the intangibles* of (c)/t willimmediately vanish.

It is the action and interactions of causal entropic force(s) upon masswithin a non-biologic pre-evolutional cosmos** that gives rise totertiary effectual* probability (aka evolution) on earth. It is thiswhat bridges the tangibles of the cosmos to the cognitive intangibles of(early) Human and (latent) Artificial Intelligence on earth (i.e. ascognitive science and computational science).

Simple probability is encountered and dealt with in our present everydaylives. It has for many centuries been a wholly incompleteunder-recognized science due to the nature of it's conflicting premises;both of which are correct, but paradoxically oppositional as to theperspectives of the frequentist versus epistemic. In reality, both arethe pre-conceptual/conceptual** precursors to entropy driven tertiaryeffectual* probability. Probability studies, when looked upon as primaryrandom (frequentists) and secondary non-random probability (epistemic)are first and second order conceptual entities and as such are confinedto past and present events. It is tertiary effectual probability thatgives rise to accountable time and has direct and indirect effects andinfluence over future events.

Utilizing the conceptual analogy of the helicoidal model (FIG. 33), theprecursors of primary and secondary conceptual probability, as well astertiary effectual probability are graphically plotted.

Even when considering the mindful introduction as given herein, theactual catalysts that initiated earthly intelligence in our everydaylives remains all but hidden. Much like Aristotle's “unmoved mover”, onemust fully examine and observe the process of ongoing evolution inmotion and over time by combining the clockwork interactions of (1)earth and (2) cosmos as above. To the cognizant observer on earth, thedynamic conceptual components and effectual consequences of the physicalinteraction of the earth within the solar system are no more obvious tothat end-observer, as the meaningless motions of the second hand of adesk clock are to the eyes of the immature three-year old child.

To perhaps now appreciate the perspective of this unique earthphenomena, one need only to remove and or significantly alter a singlephysical component, gear, or cog in the perpetual “clockwork” processdefined by (1) earth and (2) cosmos as described above (i.e. entropicdriven mass in motion), and the process will necessarily come to adisintegrating halt (going from a single tertiary helicoidal “effectualmodel” back to separate disparate systems of (a) random linear and (b)non-random circular/angular probability devoid of effect or time).

By way of mindful probability modeling a similar phenomena with both“conceptual and effectual consequences” is reproducible on a micro-earthscale when a mechanical wind-up desk clock is studied from across a roomas it sits on a desktop. The running clock, as a kinetic (entropy)driven microcosm, mechanically represents (1) earth and (2) cosmos as itoccupies space as mass and is moving in response to the concerted forcesof energy. In so doing, it parallels the cosmic entropic changes of[(a)+(b)] transitioning to (c)t. From the perspective of the cognizantobserver, the isolated desk clock has “effectual consequences” (tertiaryeffectual probability) and may influence future events, creatingnecessity and purpose for cognitive actions. Should the force of it'skinetic energy suddenly dissipate (i.e. spring physically breaks or itsimply runs down), the system immediately implodes to (1) and (2) withthe spontaneous disappearance of the energy driven dynamics of [(a)+(b)]and the immediate loss of the fictitious forces/effects of (c)t. Fromthat moment on, the clock no longer has conceptual purpose for thecognitive in room observer relative to the past, and no longer imposeseffectual probability for present or future cognitive actions. Thedormant clock does no more than inertly occupy space.

Furthermore, if two identical mechanical desk clocks are placed runningside by side on the same desk, both clocks yield consecutive moment bymoment information with their respective “1c/t” functionalities in-placeand intact. Both clocks would then be capable of providing “conceptualinsight & effectual consequences” for the cognizant soul, as they impartan understanding of the past, and give purpose to present and future,place, time and events. Now suppose in this scenario that after twohours one of these clocks without prior notice winds-down or breaks andthe other continues to run.

In the previous two hours, when running side by side both clocks yieldedconsecutive, concurrent, moment by moment information with theirrespective “c” functionality in-place and intact. Both provided tertiaryorder effectual probability giving purpose to present and future time,place(s) and events for the cognizant in room observer. With one of thetwo clocks stopping, the pre- and post-residual “conceptual insight andeffectual consequences” have dramatically changed. The stopped clock notonly sustained a complete and immediate loss of it's concerted physicalfunction, but also the complete and immediate loss of it's in-placecapacity as a generator of “c”. At that instant, it no longer conveyedor harnessed the prowess of [(a)+(b)] and offers no real conceptualhindsight nor purpose, and imposes no effectual or insightfulconsequence on the present or future environment other than occupyingspace.

Had the two clocks been acting in parallel, they could well have beenmonitoring two different, conceptually purposeful places, times andevents with effectual future consequences/ outcomes of each controlledby the cognizant in-room observer. For example, an observing andinquisitive three year old child looking on from his grandfather's knee,the sudden stoppage of the clock would likely create only a momentarylook of astonishment (as the young child recognizes only essentialrandom probability of the event i.e. clock stops), a conceptualnone-event only as the child has not developed an appreciation fornon-random recurring epistemic probability. However, the experiencedtrain yard manager on his evening shift (at the moment the clockstopped) would likely jump up from his train yard perch to immediatelysound the alarm shutting down the railroad yard in order to avertcatastrophe. The grandfather in this instance recognizes the past andpresent conceptual probability of the event i.e. potential catastrophe(a result of past epistemic conceptual non-random recurringprobability). His then present and immediate sounding of the alarm is acognitive physical event directed by tertiary effectual probability toavert another physical event at a future place and time.

This is initiated and accomplished by “effectual probability” (epistemicprobability now looking to the future) as it demonstrates how anintangible cognitive process may be used to bridge and act upon twodifferent physical events, one present and one future. Tertiaryeffectual probability is essentially the intangible of cognitiveintelligence physically acting upon a future environment/or event toreduce present and future physical chaos and uncertainty (in a whollymotion filled physical world). So in other words, a loss of two to fiveminutes on consecutive rail yard track clocks, each set to follow one oftwo moving trains coming into the yard from different directions, couldprove calamitous.

It is only when this inert broken-clock model of evolution is fullydissected and considered alongside and parallel to the functional model,that the seemingly subtle traces of disparate probabilities that create“(c)t,” may be recognized, appreciated, and then conceptually andeffectually understood from the bottom-up. The fictitious effects ofconceptual and effectual probability first appeared on our distant-pasttimeline at the inception and establishment of earth's seemingly stablecircular circadian rotation in the early solar system's post-accretionperiod:

When: (1)*+2(a)+(b)**,

-   -   goes to

1(a)+(b)*+2**,

-   -   goes to

1(c)t*+2**.

The model above represents an evolving functional earth* and it'srelative position in the solar system** as,

1(a)*+1(b)*=1(c)t*,

represents a functional earth*. It is likely that forces acting uponour * frame of reference, such as the Coriolis effect, the centrifugal,rectilinear or Euler force, or perhaps Frenet-Serret frames or rotationin a frame tied to the Universe may act as the repository for thefictitious conceptual effects/forces of effectual tertiaryprobability(s) that surround us as:

1(c)/t

By applying Occam's Razor in simplest of form, intelligence* is theresult of causal** entropic forces acting upon mass yielding tertiaryprobability* in anticipation of future chaos and uncertainty. Byapplying Occam's Razor, it is put forth that the source of our ancestralintelligence on earth* came about as the result of causal** omnipresententropic forces acting upon celestial bodies within a pre-evolutionalcosmos devoid of organization, probability, necessity, purpose, memoryand intelligence. The resultant fictitious forces/effects of tertiaryprobability* acting upon earth* gave rise to purpose, complex memory,the effectual perception of accountable time, human intelligence (early)and artificial intelligence (latent); all in order to preemptivelyanticipate and lessen future chaos and uncertainty.

The analysis herein concludes that

1(a)+(b),

-   -   goes to

1(c)/t,

where casual entropic forces in a pre-evolutional cosmos** devoid ofconcept, probability, necessity, purpose, organization, memory orintelligence gave rise to celestial* (a) random and (b) non-randomprobability. The process of evolution on earth came to be with theentropic emergence of effectual tertiary probability 1(c)/t, thereforeevolution managed to create intelligence without the use ofintelligence.

For humanity to model and or copy one of nature's truly vexing andcomplex processes, the etiological origins must first be fully vetted,sorted and understood, inclusive of the pre-founding antecedentprinciples which bring it about. As genetically bound entities, thepresent accrued intelligence has clearly seen a long and arduous journeywhose successive chains of organic evolution and eventual rise to its'present level of complexity necessarily had to occur over a multicenti-million year span.

Evolution's unrelenting primary effectual push for genetically accruedHuman Intelligence has not only now given-up and defined thepre-evolutional past point of origin, but it also provides the accruedknowledge and the retrospective proof of concept “source code” to now goforward into a latent secondary effectual era, with the genesis ofArtificial Intelligence. By understanding the antecedent point of origin1(c)/t, the progressive evolution over millions of years has physicallyarrived to have sufficient collective intelligence to perceptually andconceptually create evolution's ultimate effectual goal: artificialintelligence.

Evolution, as a dynamic organic process, has allowed perpetualearth-bound “random probability/events” to occur at all levels, althoughit is most apparent at particle, atomic, and molecular levels. A gradualhierarchal transition into “non-random recurring probability/events” ismost evident at higher functional levels as occurs in organized cells,higher organic life, and social organization. Both are perpetual andboth must be present before an organic-hierarchy for functionalintelligence via tertiary effectual probability may be attained.

The pre-evolutional** of 1(a)+1(b) going to 1(c)t* in the modeldescribed herein, allows for the coexistence of determinate andnon-determinate components, thereby perhaps appeasing, both classic andquantum physics. As a helicoidal (helical) continuum, 1(c)t may besequentially compressed to an elegantly simple and singular algorithm.

Embodiments described herein include a method comprising receiving inreal-time data of a plurality of parameters representing an entity. Themethod comprises generating micro plots that each comprise a plot of thedata for a corresponding time period of a plurality of time periods.Each time period is cyclical. The method comprises generating a modelplot comprising the micro plots plotted chronologically according to theplurality of time periods. The model plot comprises a continuous helix.The method comprises generating a prediction of a state of the entityusing characteristics of the model plot.

Embodiments described herein include a method comprising: receiving inreal-time data of a plurality of parameters representing an entity;generating micro plots that each comprise a plot of the data for acorresponding time period of a plurality of time periods, wherein eachtime period is cyclical; generating a model plot comprising the microplots plotted chronologically according to the plurality of timeperiods, wherein the model plot comprises a continuous helix; andgenerating a prediction of a state of the entity using characteristicsof the model plot

Embodiments described herein include a method comprising receivingphysiological data that includes data of a plurality of physiologicalparameters collected from an individual entity. The method comprisesgenerating a plurality of micro plots. Each micro plot comprises acyclical plot of the physiological data for a corresponding time period.Each micro plot corresponds to a time period of a plurality of timeperiods. The method comprises generating a medical model plot comprisingthe plurality of micro plots. The plurality of micro plots is plottedchronologically according to the plurality of time periods. A locationof an endpoint of each micro plot determines a change in slope of themedical model plot. The slope represents a state of health of theindividual entity.

Embodiments described herein include a method comprising: receivingphysiological data that includes data of a plurality of physiologicalparameters collected from an individual entity; generating a pluralityof micro plots, wherein each micro plot comprises a cyclical plot of thephysiological data for a corresponding time period, wherein each microplot corresponds to a time period of a plurality of time periods; andgenerating a medical model plot comprising the plurality of micro plots,wherein the plurality of micro plots are plotted chronologicallyaccording to the plurality of time periods, wherein a location of anendpoint of each micro plot determines a change in slope of the medicalmodel plot, wherein the slope represents a state of health of theindividual entity.

The physiological data is collected in real-time from sensors coupled tothe individual entity.

The sensors comprise nano-sensors.

The sensors comprise sensors coupled to the individual entity.

The sensors comprise sensors implanted in the individual entity.

The method comprises continuously collecting the physiological data.

The physiological data comprises time data.

The physiological data comprises location data.

The physiological data comprises physical activity data.

The time period of the cyclical plot is a 24-hour period.

The cyclical plot is based on a circadian cycle.

The micro plot for each time period comprises a start point and theendpoint.

The endpoint of each micro plot is located at a same point in a completerotation that defines the micro plot.

The endpoint of each micro plot for each time period is located at a newposition in space.

The physiological data determines the new position of the endpoint.

The endpoint of a micro plot is a start point for a next subsequentmicro plot.

The medical model plot comprises a continuous helix comprising theplurality of micro plots.

The method comprises compressing the data of the plurality of microplots to form the medical model plot.

The method comprises determining the state of health by comparing atleast one set of micro plots of the medical model plot.

Changes in the slope indicate physical changes in the state of health ofthe individual entity.

The slope of the medical model plot is inversely proportional to aquality of life of the individual entity.

The slope of the medical model plot represents longevity of theindividual entity.

The medical model plot comprises a start point that corresponds to birthof the individual entity.

The medical model plot comprises a normal zone, wherein the normal zonerepresents absence of disease process in the individual entity.

The medical model plot comprises a subclinical zone, wherein thesubclinical zone represents onset of clinical symptoms in the individualentity.

The medical model plot comprises a clinical zone, wherein the clinicalzone represents presence of clinical symptoms in the individual entity.

The medical model plot comprises an endpoint that corresponds to deathof the individual entity.

The method comprises providing the medical model plot to the individualentity.

The method comprises providing the medical model plot to at least onehealthcare provider.

The method comprises providing the medical model plot to at least oneorganization.

Embodiments described herein include a system comprising a plurality ofsensors coupled to an individual entity. The plurality of sensorscollects physiological data that includes data of a plurality ofphysiological parameters collected from the individual entity. Thesystem includes a platform comprising a processor. The platform iscoupled to the plurality of sensors. The processor is running anapplication, and the application generates a plurality of micro plots.Each micro plot comprises a cyclical plot of the physiological data fora corresponding time period. Each micro plot corresponds to a timeperiod of a plurality of time periods. The application generates amedical model plot comprising the plurality of micro plots. Theplurality of micro plots is plotted chronologically according to theplurality of time periods. A location of an endpoint of each micro plotdetermines a change in slope of the medical model plot. The sloperepresents a state of health of the individual entity.

Embodiments described herein include a system comprising: a plurality ofsensors coupled to an individual entity, wherein the plurality ofsensors collect physiological data that includes data of a plurality ofphysiological parameters collected from the individual entity; and aplatform comprising a processor, wherein the platform is coupled to theplurality of sensors, wherein the processor is running an application,wherein the application generates a plurality of micro plots, whereineach micro plot comprises a cyclical plot of the physiological data fora corresponding time period, wherein each micro plot corresponds to atime period of a plurality of time periods, wherein the applicationgenerates a medical model plot comprising the plurality of micro plots,wherein the plurality of micro plots are plotted chronologicallyaccording to the plurality of time periods, wherein a location of anendpoint of each micro plot determines a change in slope of the medicalmodel plot, wherein the slope represents a state of health of theindividual entity.

The physiological data is collected in real-time from the plurality ofsensors.

The sensors comprise nano-sensors.

The sensors comprise sensors coupled to the individual entity.

The sensors comprise sensors implanted in the individual entity.

The system comprises continuously collecting the physiological data.

The physiological data comprises time data.

The physiological data comprises location data.

The physiological data comprises physical activity data.

The time period of the cyclical plot is a 24-hour period.

The cyclical plot is based on a circadian cycle.

The micro plot for each time period comprises a start point and theendpoint.

The endpoint of each micro plot is located at a same point in a completerotation that defines the micro plot.

The endpoint of each micro plot for each time period is located at a newposition in space.

The physiological data determines the new position of the endpoint.

The endpoint of a micro plot is a start point for a next subsequentmicro plot.

The medical model plot comprises a continuous helix comprising theplurality of micro plots.

The data of the plurality of micro plots is compressed, and the medicalmodel plot comprises the compressed data.

The state of health by is determined by comparing at least one set ofmicro plots of the medical model plot.

The change in the slope corresponds to physical changes in the state ofhealth of the individual entity.

The slope of the medical model plot is inversely proportional to aquality of life of the individual entity.

The slope of the medical model plot corresponds to longevity of theindividual entity.

The medical model plot comprises a start point that corresponds to birthof the individual entity.

The medical model plot comprises a normal zone, wherein the normal zonerepresents absence of disease process in the individual entity.

The medical model plot comprises a subclinical zone, wherein thesubclinical zone represents onset of clinical symptoms in the individualentity.

The medical model plot comprises a clinical zone, wherein the clinicalzone represents presence of clinical symptoms in the individual entity.

The medical model plot comprises an endpoint that corresponds to deathof the individual entity.

The medical model plot is provided to the individual entity.

The medical model plot is provided to at least one healthcare provider.

The medical model plot is provided to at least one organization.

Computer systems and networks suitable for use with the dMEM embodimentsdescribed herein include local area networks (LAN), wide area networks(WAN), Internet, or other connection services and network variationssuch as the world wide web, the public internet, a private internet, aprivate computer network, a public network, a mobile network, a cellularnetwork, a value-added network, and the like. Computing devices coupledor connected to the network as a component of progressive mechanicalintelligence embodiments may be any microprocessor controlled devicethat permits access to the network, including terminal devices, such aspersonal computers, workstations, servers, mini computers, main-framecomputers, laptop computers, mobile computers, palm top computers, handheld computers, mobile phones, TV set-top boxes, or combinationsthereof. The computer network may include one of more LANs, WANs,Internets, and computers. The computers may serve as servers, clients,or a combination thereof.

The dMEM can be a component of a single system, multiple systems, and/orgeographically separate systems. The dMEM can also be a subcomponent orsubsystem of a single system, multiple systems, and/or geographicallyseparate systems. The dMEM can be coupled to one or more othercomponents (not shown) of a host system or a system coupled to the hostsystem.

One or more components of the dMEM and/or a corresponding system orapplication to which the dMEM is coupled or connected includes and/orruns under and/or in association with a processing system. Theprocessing system includes any collection of processor-based devices orcomputing devices operating together, or components of processingsystems or devices, as is known in the art. For example, the processingsystem can include one or more of a portable computer, portablecommunication device operating in a communication network, and/or anetwork server. The portable computer can be any of a number and/orcombination of devices selected from among personal computers, personaldigital assistants, portable computing devices, and portablecommunication devices, but is not so limited. The processing system caninclude components within a larger computer system.

The processing system of an embodiment includes at least one processorand at least one memory device or subsystem. The processing system canalso include or be coupled to at least one database. The term“processor” as generally used herein refers to any logic processingunit, such as one or more central processing units (CPUs), digitalsignal processors (DSPs), application-specific integrated circuits(ASIC), etc. The processor and memory can be monolithically integratedonto a single chip, distributed among a number of chips or components,and/or provided by some combination of algorithms. The methods describedherein can be implemented in one or more of software algorithm(s),programs, firmware, hardware, components, circuitry, in any combination.

The components of any system that includes the dMEM can be locatedtogether or in separate locations. Communication paths couple thecomponents and include any medium for communicating or transferringfiles among the components. The communication paths include wirelessconnections, wired connections, and hybrid wireless/wired connections.The communication paths also include couplings or connections tonetworks including local area networks (LANs), metropolitan areanetworks (MANs), wide area networks (WANs), proprietary networks,interoffice or backend networks, and the Internet. Furthermore, thecommunication paths include removable fixed mediums like floppy disks,hard disk drives, and CD-ROM disks, as well as flash RAM, UniversalSerial Bus (USB) connections, RS-232 connections, telephone lines,buses, and electronic mail messages.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense as opposed to anexclusive or exhaustive sense; that is to say, in a sense of “including,but not limited to.” Words using the singular or plural number alsoinclude the plural or singular number respectively. Additionally, thewords “herein,” “hereunder,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. When theword “or” is used in reference to a list of two or more items, that wordcovers all of the following interpretations of the word: any of theitems in the list, all of the items in the list and any combination ofthe items in the list.

The above description of embodiments of the dMEM and correspondingsystems and methods is not intended to be exhaustive or to limit thesystems and methods to the precise forms disclosed. While specificembodiments of, and examples for, the dMEM and corresponding systems andmethods are described herein for illustrative purposes, variousequivalent modifications are possible within the scope of the systemsand methods, as those skilled in the relevant art will recognize. Theteachings of the dMEM and corresponding systems and methods providedherein can be applied to other systems and methods, not only for thesystems and methods described above. The elements and acts of thevarious embodiments described above can be combined to provide furtherembodiments. These and other changes can be made to the dMEM andcorresponding systems and methods in light of the above detaileddescription.

1. (canceled)
 2. A method comprising: configuring a plurality ofnanosensors to be carried on and entity and to collect data of aplurality of parameters representing the entity; receiving in real-timethe collected data of the plurality of parameters; generating microplots that each comprise a plot of the collected data for acorresponding time period of a plurality of time periods, wherein eachmicro plot integrates the collected data for the time period into acyclical plot of the plurality of parameters, and a location of anendpoint of each micro plot is determined by a shape of the micro plot;generating a model plot including a life cycle line comprising acontinuous helix formed by consecutively plotting the micro plots inchronological order according to the plurality of time periods, whereina slope of the life cycle line is determined by the locations of theendpoints of the micro plots; and predicting with the slope of the lifecycle line medical profiles of the entity for future time periods beyondthe plurality of time periods, wherein a decrease in the slope indicatesat least one of better quality of life and longer life, and an increasein the slope indicates at least one of worse quality of life and shorterlife, wherein the medical profiles include predictions of physiologicalevent data at future time periods in the life of the entity, andoutputting the prediction for use in preventative treatment of theentity.
 3. The method of claim 2, comprising continuously collecting thedata of the plurality of parameters.
 4. The method of claim 2, whereinthe data of the plurality of parameters comprises time data, locationdata, physiological data, and physical activity data.
 5. The method ofclaim 2, wherein the time period is a 24-hour period.
 6. The method ofclaim 2, wherein the cyclical plot is based on a circadian cycle.
 7. Themethod of claim 2, wherein the endpoint of each micro plot for each timeperiod is located at a position in space as determined by data of theplurality of parameters of that time period.
 8. The method of claim 7,wherein the endpoint of a micro plot is a start point for a nextsubsequent micro plot.
 9. The method of claim 8, comprising compressingthe data of the plurality of micro plots to form the medical model plot.10. The method of claim 2, comprising determining a state of health ofthe entity by comparing at least one set of micro plots of the medicalmodel plot.
 11. The method of claim 2, wherein changes in the slopeindicate physical changes in the state of health of the individualentity.
 12. The method of claim 2, wherein the slope of the model plotis inversely proportional to a quality of life of the individual entity.13. The method of claim 2, wherein the slope of the model plot isconfigured to represent longevity of the individual entity.
 14. Themethod of claim 2, wherein the model plot comprises a start pointconfigured to correspond to birth of the individual entity.
 15. Themethod of claim 14, wherein the model plot comprises a normal zoneconfigured to represent absence of disease process in the individualentity.
 16. The method of claim 15, wherein the model plot comprises asubclinical zone configured to represent onset of clinical symptoms inthe individual entity.
 17. The method of claim 16, wherein the modelplot comprises a clinical zone configured to represent presence ofclinical symptoms in the individual entity.
 18. The method of claim 2,wherein the model plot comprises an endpoint configured to correspond todeath of the individual entity.
 19. The method of claim 2, comprisingoutputting the medical model plot to at least one of the entity and atleast one healthcare provider.
 20. The method of claim 2, wherein the ofthe life cycle line correlates to actions and behaviors of the entityduring the plurality of time periods.